A seismic attribute is a measurable property of seismic data used to highlight or identify geological or geophysical features. Sets of attributes are further useful either for supervised or unsupervised classification to partition the data into distinct regions, or for data mining to find regions compatible with a prescribed pattern. Such classification can easily create hundreds of regions and an automated process of ranking the regions allows the interpreter to focus on the more promising ones. A partial review of published use of seismic attributes follows.
U.S. Pat. No. 5,850,622 (“Time-Frequency Processing and Analysis of Seismic Data Using Very Short-Time Fourier Transforms”) to Vassiliou and Garossino discloses a method of removing or attenuating seismic noise that can also be used for seismic attribute analysis and automatic trace editing. The method applies a Very Short-Time Fourier Transform (VSTFT) to replicate one broadband trace into many near single-frequency “sub-band” traces.
U.S. Pat. No. 5,940,778 (“Method Of Seismic Attribute Generation And Seismic Exploration”) to Marfurt et al. discloses methods of quantifying and visualizing structural and stratigraphic features in three dimensions through the use of eigenvector and eigenvalue analyses of a similarity matrix. It further discloses the use of seismic attributes derived from similarity matrices to detect the conditions favorable for the origination, migration, accumulation, and presence of hydrocarbons in the subsurface.
U.S. Pat. No. 6,226,596 (“Method For Analyzing And Classifying Three Dimensional Seismic Information”) to Gao discloses a method to capture and characterize three dimensional seismic reflection patterns based volume-seismic textures valuated using a Voxel Coupling Matrix (VCM). To extract the VCM seismic textural information at a specific location, a finite number of neighboring voxels are processed to create the VCM. The VCM is then processed to create to texture attributes. Such attribute volumes are subsequently used classified to produce a seismic interpretation volume.
U.S. Pat. No. 6,278,949 (“Method For Multi-Attribute Identification Of Structure And Stratigraphy In A Volume Of Seismic Data”) to Alam discloses a method for the visual exploration of a seismic volume without horizon picking or editing, but that still displays all horizons with their stratigraphic features and lithologic variations. Seismic data are processed to generate multiple attributes at each event location with a specified phase of the seismic trace. Subsets of multiple attributes are then interactively selected, thresholded, and combined with a mathematical operator into a new volume displayed on a computer workstation. Manipulation of attribute volumes and operators allows the user to recognize visually bodies of potential hydrocarbon reservoirs.
U.S. Pat. No. 6,438,493 (“Method For Seismic Facies Interpretation Using Textural Analysis And Neural Networks”) to West and May discloses a method for segmentation based on seismic texture classification. For a prescribed set of seismic facies in seismic data volume, textural attributes are calculated and used to train a probabilistic neural network. This neural network is then used to classify each voxel of the data, which in practice segments the data into the different classes. Further, U.S. Pat. No. 6,560,540 (“Method For Mapping Seismic Attributes Using Neural Networks”) to West and May discloses a method for classification of seismic data during the seismic facies mapping process.
U.S. Pat. No. 6,594,585 (“Method Of Frequency Domain Seismic Attribute Generation”) to Gerszetenkorn discloses a method of generating attributes from seismic data. The central idea is that the amplitude or phase spectrum of a short-window Fourier transform is fit with a model curve whose parameters are used as seismic attributes.
U.S. Pat. No. 6,628,806 (“Method For Detecting Chaotic Structures in a Given Medium”) to Keskes and Pauget discloses a method of detecting chaotic structures in seismic data based on the variability of the gradient vectors, or to be more specific, the eigenvalues computed from a local sum of dyadic gradient-vector products.
U.S. Pat. No. 6,745,129 (“Wavelet-Based Analysis of Singularities in Seismic Data”) to Li and Liner discloses a wavelet-based method for the analysis of singularities of seismic data. A wavelet transform is applied to seismic data and the Holder exponent is calculated for every time point of the wavelet transform. The Holder exponents plotted versus time are utilized in place of seismic traces for visualization because they appear to highlight stratigraphic boundaries and other geological features.
U.S. Pat. No. 7,398,158 (“Method and Apparatus for Detecting Fractures Using Frequency Data Derived from Seismic Data”) to Najmuddin discloses a method to map fractures in an Earth formation. This method uses the frequency spectra derived from P-wave seismic data over a pair of specific time windows above and below a seismic horizon to infer the presence or absence of fractures based on the attenuation of high frequencies as measured by the shift in frequency spectra from higher frequencies to lower ones.
U.S. Patent Application No. 2007/0223788 (“Local Dominant Wave-Vector Analysis of Seismic Data”) by Pinnegar et al. discloses a method for processing multi-dimensional data to determine frequency-dependent features therein. The multi-dimensional signal data are transformed into space-frequency or time-space-frequency domain using either a Full Polar S-Transform (FPST) or Sparse Polar S-Transform (SPST) to determine the dominant component and its orientation, which allows generation of a dip map, a frequency map, or an amplitude map.
PCT Patent Application Publication No. WO 2008/130978 (“Methods of Hydrocarbon Detection Using Spectral Energy Analysis”) by Wiley et al. discloses a method for detecting hydrocarbons based on the dominant frequency and bandwidth at and near the target area.
PCT Patent Application Publication No. WO 2009/011735 (“Geologic Features from Curvelet Based Seismic Attributes”) by Neelamani and Converse discloses a method for identifying geologic features from seismic data by taking a curvelet transform of the data. From this curvelet representation, selected geophysical data attributes and their interdependencies are extracted that are used to identify geologic features.
Pitas and Kotropoulos (“Texture Analysis and Segmentation of Seismic Images”, International Conference on Acoustics, Speech, and Signal Processing, 1437-1440 (1989)) propose a method for the texture analysis and segmentation of geophysical data based on the detection of seismic horizons and the calculation of their attributes (e.g. length, average reflection strength, signature). These attributes represent the texture of the seismic image. The surfaces are clustered into classes according to these attributes. Each cluster represents a distinct texture characteristic of the seismic image. After this initial clustering, the points of each surface are used as seeds for segmentation where all pixels in the seismic image are clustered in those classes in accordance to their geometric proximity to the classified surfaces.
Simaan (e.g., “Knowledge-Based Computer System for Segmentation of Seismic Sections Based on Texture”, SEG Expanded Abstracts 10, 289-292 (1991)) disclose a method for the segmentation of two-dimensional seismic sections based on the seismic texture and heuristic geologic rules.
Fernandez et al. (“Texture Segmentation of a 3D Seismic Section with Wavelet Transform and Gabor Filters”, 15th International Conference on Pattern Recognition, 354-357 (2000)) describe a supervised segmentation (i.e., classification) of a 3D seismic section that is carried out using wavelet transforms. Attributes are computed on the wavelet expansion and on the wavelet-filtered signal, and used by a classifier to recognize and subsequently segment the seismic section. The filters are designed by optimizing the classification of geologically well understood zones. As a result of the segmentation, zones of different internal stratification are identified in the seismic section by comparison with the reference patterns extracted from the representative areas.
Patel et al., (“The Seismic Analyzer: Interpreting and Illustrating 2D Seismic Data”, IEEE Transactions on Visualization and Computer Graphics 14, 1571-1578 (2008)) disclose a toolbox for the interpretation and illustration of two-dimensional seismic slices. The method precalculates the horizon structures in the seismic data and annotates them by applying illustrative rendering algorithms such as deformed texturing and line and texture transfer functions.
Randen and Sonneland (“Atlas of 3D Seismic Attributes”, in Mathematical Methods and Modeling in Hydrocarbon Exploration and Production, Iske and Randen (editors), Springer, 23-46 (2005)) present an overview of three-dimensional seismic attributes that characterize seismic texture or seismostratigraphic features.
In “Coherence-derived volumetric curvature using the Windowed Fourier Transform,” Zhang performs the windowed Fourier transform in 1D using a 1D window to obtain a volumetric curvature attribute, which gives improved ability to identify geologic structure, faults and fractures. (71st EAGE Conference, Amsterdam, The Netherlands, Jun. 8-11, 2009, paper 275)
What is needed is a method that distinguishes different regions of the seismic data based on their seismic texture, preferably partitions the data into the different regions in an automated manner, and ideally even ranks the regions based on their potential to contain hydrocarbons. The present invention satisfies this need